The present disclosure relates generally to Advanced Process Control (“APC”) as applied to semiconductor fabrication and, more particularly, to system and method for implementing a Virtual Metrology (“VM”) APC platform.
APC has become an essential component in semiconductor fabrication for enabling continued improvement of device yield and reliability at a reduced cost. Significant elements of APC include integrated metrology, fault detection and classification, and run-to-run (“R2R”) control. APC aids in reducing process variation as well as production costs. A key requirement for effective APC is that metrology tools must be available to measure key parameters within an acceptable time frame. Additionally, methods must be provided for analyzing and interpreting measurement data produced by the metrology tools. In practice, APC requires rich in-line measurements because the manufacturing processes are usually subjected to disturbance and drift. In the past, one of the primary limitations on APC at the wafer-to-wafer (“W2W”) level has been the nonavailability of timely metrology data at that level. VM techniques have alleviated this problem to some degree.
The basic theory of operation for VM is that, in a development flow, a range of production runs is used to develop an empirical prediction model that correlates to actual measurements from metrology tools with the process trace data that was present at the time. The model is refined until the metrology values it predicts show a reasonable correlation to the actual measurement data. Once the model is developed, the VM system goes “live,” or “online,” in the production flow and is used during execution of a process runs to estimate metrology values for wafers being processed. Variations from the desired targets are used to update recipe parameters in a traditional R2R control fashion. These updates, or adjustments, can be made on a W2W basis without requiring a separate measurement step to be performed. It will be recognized that actual metrology measurements are still made in the production flow; however, their role is primarily that of calibrating/updating the prediction model, rather than as the primary control checkpoint for the process.